Qusim.py – A toy multi-qubit quantum computer simulator written in Python | Hacker News Reader

Qusim.py – A toy multi-qubit quantum computer simulator written in Python

github.com

242 pointsadamisntdead44 comments

For the uninitiated - to simulate a basic quantum computer, all you need is the following:

* A product state vector, which is a vector of size 2^n filled with complex numbers where n is the number of qbits you're using. You derive the product state from a series of individual qbits by taking their tensor product; this exponential term is why simulating quantum computers takes exponential space.

* A set of common quantum logic gates, which are (2^n)x(2^n) matrices you multiply against the product state vector to derive the new product state vector; these matrices must be unitary (their conjugate-transpose is their inverse) (edited, see [0]) and therefore reversible (quantum computers are reversible computers). The full (2^n)x(2^n) matrices are derived by taking the tensor products of several 2x2 and 4x4 matrices.

* The measurement logic, where you calculate how the product state collapses by taking the square of the absolute value of each entry in the product state (these entries are called amplitudes).

This project is an implementation of those three things in Python. There is nothing special about the above mathematical constructs save that they match the observed semantics of a quantum system. This is good! Quantum computing is accessible to anyone who has taken a basic undergraduate course in linear algebra.

[0] I previously believed all quantum operators were their own inverses (Unitary and Hermitian) but apparently that is not the case; see comments below.

That's pretty much it! There obviously is loads of optimizations that can be done, but it obscures the pretty standard linear algebra that is used to represent and calculate the states of the quantum computer!

A well-written version using OpenCL and written in rust is also available: http://github.com/qcgpu/qcgpu-rust if you are interested! Always looking for feedback

I've wondered - is it a common memory optimization tactic to store the individual qbit values until they become entangled and the product state cannot be factored? Or is entanglement common enough that it wouldn't be worth the overhead?

It depends! If you use a lot of scratch qubits, it can be useful to use sparse vectors, but if you are going to be doing a lot of superpositions / entangling many qubits, this isn't as effective, and performance suffers a lot. Libraries such as libquantum (written in C) use sparse state vectors, whereas the QCGPU library uses dense state vectors (as it is being stored on the GPU, it doesn't really impact the performance of the computer it's being simulated on). I benchmarked it, and the rust one was way faster for everything, apart from the initialization of registers and the application of some controlled gates

This is awesome. Over time I have came across too many posts claiming to explain QCs to hackers and they go on pages and pages without distilling this simple little thing. It would be great if you can also implement something like Shore's algo with your simulator.

You can, but it would be very slow! This implementation gets really really slow around 15/16 qubits.

I am currently working on implementing shors algorithm into my main, simulation library. I already have grovers algorithm! https://qcgpu.github.io/qcgpu-rust/book/algorithms/grover.ht...

Might it be caused by the use of slow matrix operation algorithms rather than the size?

> these matrices must be their own inverse (in mathematical terms, Unitary and Hermitian). The full (2^n)x(2^n) matrices are derived by taking the tensor products of several 2x2 and 4x4 matrices.

Gates don't have to be Hermitian right? Like NOT^(1/2) is not Hermitian.

Yep, observables in quantum mechanics are represented by hermitian matrices

Oh wow, am I wrong? I thought all quantum operators were their own inverses, which since they're all unitary means they must be hermitian.

>these matrices must be their own inverse (in mathematical terms, Unitary and Hermitian)

Not necessarily. The only requirement on quantum operators is that they must be unitary. Only observables have to be Hermitian (so they have real eigenvalues).

Thanks for the correction! I thought all quantum operators were their own inverse, but I guess I was wrong.

Neither unitary or hermitian means that operators are there own inverse. Unitary here means that the inverse is the same as the complex transpose - which means that there is an inverse and it is easy to find. This is important as quantum circuits must be composed of unitaries, i.e. the circuit is reversable. Hermitian operators have no particular special property with respect to inverses.

Quantum gates are unitary because of conservation of probability (the out state must be remain normalized to 1). If it's also hermitian (equal to conjugate transpose) then it's its own inverse.

Thanks for the description but that made very little sense to a 'regular' developer like me. Any chance you could break it down and bring it down to my level?

I recommend the textbook Quantum Computing for Computer Scientists.

I would also have to recommend "Quantum Mechanics, The Theoretical Minimum" by Leonard Susskind, if you wish to go a little bit further into general quantum mechanics

You should check out these videos, one of them might give you an understanding!

- https://www.youtube.com/watch?v=JhHMJCUmq28 | Quantum Computers Explained - https://www.youtube.com/watch?v=IrbJYsep45E | The Mathematics of Quantum Computers - https://www.youtube.com/watch?v=wUwZZaI5u0c | Hacking at Quantum Speed with Shor's Algorithm - https://www.youtube.com/playlist?list=PL1826E60FD05B44E4 | A series by the author of the standard textbook

These are videos I particularly like!

Quantum Computation and Quantum Information, a.k.a "Mike and Ike" is the authoritative textbook in the field. It also happens to be very readable and beginner friendly. It's well worth reading the introduction to QC in the first twelve pages and skimming the first sixty. And the rest, as time/interest permits.

This sounds snide, but please believe me my intention is to be helpful.

Have you considered instead raising your level? The relevant topic, as stated in the comment, is linear algebra.

My experience with linear algebra is that it's possible to get an excellent grade in class, without learning anything, provided the tests are multiple choice...

I'm no expert, but SMBC covers quantum computers quite well too :-)

https://www.smbc-comics.com/comic/the-talk-3

The red button was a nice touch. I genuinely laughed.

After reading that article, you pretty much become an expert. I'm going to go apply to dwave now.

This is a great succinct explanation. I definitely understand QC's better after reading it. Thanks.

Cool! My April fools day joke this year was a quantum programming language made up entirely of "entanglement" and "superposition": https://github.com/dabacon/qsel The simulator takes about 150 lines of python as well.

Hey Dave, your lecture notes [0] helped us a lot when we were building our simulator [1]. Thank you!

[0]: https://courses.cs.washington.edu/courses/cse599d/06wi/

[1]: https://github.com/QCHackers/qchackers/tree/master/software/...

That class was so many careers ago. Glad you enjoyed the notes. I wonder if I taught it today: maybe I'd start by having students use a simulator, or maybe trying to write a simple simulator themselves!

Hey that code looks familiar :)

There is this quote by Jürgen Schmidhuber, director at the Swiss AI Lab IDSIA , creator of LSTM Neural Nets that power many of our current machine learning applications.

" General purpose quantum computation won’t work (my prediction of 15 years ago is still standing). Related: The universe is deterministic, and the most efficient program that computes its entire history is short and fast, which means there is little room for true randomness, which is very expensive to compute. What looks random must be pseudorandom, like the decimal expansion of Pi, which is computable by a short program. Many physicists disagree, but Einstein was right: no dice. There is no physical evidence to the contrary randomness.html[http://people.idsia.ch/~juergen/randomness.html].

For example, Bell’s theorem does not contradict this. And any efficient search in program space for the solution to a sufficiently complex problem will create many deterministic universes like ours as a by-product. Think about this. More here computeruniverse.html [http://people.idsia.ch/~juergen/computeruniverse.html] and here.

I am out of depth with regards to quantum theory and quantum computation. If anyone with better knowledge with regards to quantum computation can clarify it would be great.

Is there any rationale to support Schmidhuber's argument, and has any advance in making actual quantum computers disprove his theory?

Hint: "There is no physical evidence to the contrary" isn't really a proof.

You are getting down-voted because you are shifting the burden of proof.

It wasn't me that was shifting the burden of proof!

It was the original quote from Schmidhuber, claiming as if it were a known fact "The universe is deterministic, and the most efficient program that computes its entire history is short and fast", backed by the (arguable) argument "There is no physical evidence to the contrary."

Whether or not the universe is deterministic, quantum computers will work as long as nature works by wavefunctions and unitary matrices. There are many good reasons for believing this to be true, including experiments. Real randomness is not a requirement.

I have looked into this somewhat deeply and he might have a point, but it's a bit subtle. First, current computers are not powerful enough to show him wrong. Second, the Many Worlds interpretation of QM is both deterministic and quantum, and since Jurgen is assuming his super-program creates a multiverse anyway, it's a logical next step.

The meat of Jurgen's proposal is that "fast" programs are more likely (which makes sense, IMHO) and quantum universes are too wasteful computationally (which seems less obvious).

If you want to read more along these lines, I can recommend something quite interesting from last year. It references Jurgen but argues that computational speed doesn't matter, only the length of the algorithm needed to produce our subjective experiences:

Could the physical world be emergent instead of fundamental, and why should we ask? (short version) by Markus P. Mueller https://arxiv.org/abs/1712.01816

Schmidhuber doesn't seem to be a physicist, so I'll go with the majority of physicists on this one. Currently there is no reason to assume that quantum computers are impossible. There might be engineering challenges that we can't overcome, but quantum mechanics is a decently well established field and unless its assumptions are fundamentally wrong, quantum computers should work.

Sounds like a crackpot to be honest. QC doesn't work yet because of practical limitations, but individual blocks have been shown to work separately and even small computers [1]. In looking at his wording he may be addressing a straw man, because QC has never aimed to be a general purpose general computer.

The universe is usually thought of as not deterministic. The deterministic interpretations out there come with huge problems: non-locality. Basically, that all particles in the universe are connected with a wave of sorts that acts instantaneously no matter the distance. But this is a philosophical distinction at best. Quantum computers work just fine.

[1]https://www.technologyreview.com/s/609451/ibm-raises-the-bar...

Is it possible that a massive array of GPUs might be more economical than a few real quantum chips that need to be cooled to absolute zero?

That absolutely is the case right now, it may not be in a few decades.

In the short term, yes, while the number of qubits is small and even smaller when you consider logical qubits require multiple real qubits to account for error.

In simplified terms though, for every logical qubit you add to the system, you effectively double the power of the machine. To double the power of your GPU stack, you'd need 2x more GPUSs each time. There is also the additional caveat of the type of problem - not every problem currently suited for a gpu is going to be better run on a qpu.

That depends on the number of qbits you want to simulate. Doing the calculations on classical computers has an exponential slowdown as you need 2^n classical bits to store n qbits. It's pretty unlikely that you will ever be able to simulate a quantum computer with more than a few dozen qbits.

Indeed. I'd expect this to top of at about 64 qbits with smart maths. (Mostly due to pointer limitations.) You could try to extend addressing but it'll be even slower...

Just as a data point: Current state-of-the-art methods for Lanczos-type methods on state vectors are around 40 spins/qubits incorporating many symmetries and additional shortcuts one does not have in a generic "quantum computer simulator". Without those symmetries/shortcuts, the limit is likely closer to 30 qubits (i.e. approx. 16-64 GB per state vector), as it’s not only necessary to store these beasts but also do operations on them.

If you don’t insist on a single dense state vector but a sparser tensor network-based formulation, you can go to much larger systems (hundreds or thousands of spins) but will be limited by the amount of entanglement you can represent in your system.

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